Unlock the Power of Machine Learning: Learn the Techniques that are Revolutionizing Industry!
Machine Learning is transforming the world as we know it. From improving healthcare to predicting market trends, this innovative technology…
The Future Is Here: The Latest News And Developments In The World Of AI!
Artificial intelligence (AI) is rapidly evolving, and it is becoming an integral part of our daily lives. From business to…
Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture
arXiv:2605.27373v1 Announce Type: new Abstract: As intelligent systems become more autonomous, the scientific community focuses on creating decision-making mechanisms that…
LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding
arXiv:2605.27365v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing…
Learning to Assign Prediction Tasks to Agents with Capacity Constraints
arXiv:2605.27999v1 Announce Type: cross Abstract: We address the problem of learning to assign prediction tasks to one agent from a…
Where Does Toxicity Live? Mechanistic Localization and Targeted Suppression in Language Models
arXiv:2605.27997v1 Announce Type: cross Abstract: Large language models frequently generate toxic, hateful, or harmful content, yet existing mitigation methods rely…
PilotTTS: A Disciplined Modular Recipe for Competitive Speech Synthesis
arXiv:2605.27258v2 Announce Type: replace-cross Abstract: Building state-of-the-art text-to-speech (TTS) systems typically demands millions of hours of proprietary data and complex…
Position: Retire the “Positive Backdoor” Label — Secret Alignment Requires Strict and Systematic Evaluation
arXiv:2605.28597v1 Announce Type: cross Abstract: This position paper argues that the AI/ML community should stop overclaiming and retire the label…
How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving
arXiv:2605.28302v1 Announce Type: cross Abstract: Modern large language model (LLM) inference has progressively disaggregated to keep pace with growing model…
Position: The Turing-Completeness of Autoregressive Transformers Relies Heavily on Context Management
arXiv:2605.19514v2 Announce Type: replace Abstract: Many works make the eye-catching claim that Transformers are Turing-complete. However, the literature often conflates…
RL Squeezes, SFT Expands: A Comparative Study of Reasoning LLMs
arXiv:2509.21128v2 Announce Type: replace Abstract: Large language models (LLMs) are typically trained by reinforcement learning (RL) with verifiable rewards (RLVR)…
ASTRA: Communication-Efficient Acceleration for Multi-Device Transformer Inference
arXiv:2505.19342v2 Announce Type: replace-cross Abstract: Multi-device inference can reduce Transformer latency by parallelizing computation. However, existing methods require high inter-device…